Efficient Hierarchical Agglomerative Clustering Algorithms on GPU Using Data Partitioning

We explore the capabilities of today's high-end Graphics processing units (GPU) on desktops to efficiently perform hierarchical agglomerative clustering (HAC) through partitioning of data. Traditional HAC has high time and memory complexities leading to low clustering efficiencies. We reduce time and memory bottlenecks of the traditional HAC algorithm by exploring the performance capabilities of the GPU, significantly accelerating the computations without compromising the accuracy of clusters. We implement the traditional HAC and the Partially Overlapping Partitioning (PoP) on GPU using Compute Unified Device Architecture (CUDA) and compare the computational performance with CPU using micro array data. The result shows that the PoP HAC and traditional HAC are up to 442 times and 66 times faster on the GPU respectively than the time taken by CPU. The PoP-enabled HAC on GPU requires only a fraction of the memory required by traditional HAC both on the CPU and GPU.